Initial commit

args.yml

@@ -16,7 +16,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb/
+    - seals_experts_wandb_oldpickle/seed_5/
   - - log_interval
     - -1
   - - max_total_trials
@@ -56,7 +56,7 @@
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Walker2d-v0__ppo__5__1658509319
+    - runs/seals/Walker2d-v0__ppo__5__1658860231
   - - track
     - true
   - - trained_agent
@@ -72,4 +72,4 @@
   - - wandb_entity
     - null
   - - wandb_project_name
-    - seals-experts
+    - seals-experts-oldpickle

ppo-seals-Walker2d-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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 size 1753237

ppo-seals-Walker2d-v0/data

@@ -4,19 +4,19 @@
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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fd223da7790>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd223da7820>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd223da78b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd223da7940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fd223da79d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fd223da7a60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd223da7af0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fd223da7b80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd223da7c10>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd223da7ca0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd223da7d30>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fae1fbbfca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fae1fbbfd30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fae1fbbfdc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fae1fbbfe50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fae1fbbfee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fae1fbbff70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fae1fbbc040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fae1fbbc0d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fae1fbbc160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fae1fbbc1f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fae1fbbc280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fd223d9dcc0>"
+        "_abc_impl": "<_abc_data object at 0x7fae1fbc1090>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -68,12 +68,12 @@
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1658509322.696638,
+    "start_time": 1658860236.103207,
     "learning_rate": {
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     },
-    "tensorboard_log": "runs/seals/Walker2d-v0__ppo__5__1658509319/seals-Walker2d-v0",
+    "tensorboard_log": "runs/seals/Walker2d-v0__ppo__5__1658860231/seals-Walker2d-v0",
     "lr_schedule": {
         ":type:": "<class 'function'>",
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@@ -93,7 +93,7 @@
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results.json

@@ -1 +1 @@
-{"mean_reward": 3714.7483351, "std_reward": 374.4360849549338, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-25T13:12:31.651728"}
+{"mean_reward": 3714.7483351, "std_reward": 374.4360849549338, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-27T16:51:30.692783"}

train_eval_metrics.zip

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